abstract = "Optimised models of complex physical systems are
difficult to create and time consuming to optimise. The
physical and business processes are often not well
understood and are therefore difficult to model. The
models of often too complex to be well optimized with
available computational resources. Too often
approximate, less than optimal models result. This work
presents an approach to this problem that blends three
well-tested components. First: We apply Linear Genetic
Programming (LGP) to those portions of the system that
are not well understood -- for example, modelling data
sets, such the control settings for industrial or
chemical processes, geotechnical property prediction or
UXO detection. LGP builds models inductively from known
data about the physical system. The LGP approach we
highlight is extremely fast and builds rapid to
execute, high-precision models of a wide range of
physical systems. Yet it requires few parameter
adjustments and is very robust against overfitting.
Second: We simulate those portions of the system -- for
example, the cost model for the processes -- these are
well understood with human built models. Finally: We
optimise the resulting meta-model using Evolution
Strategies (ES). ES is a fast, general-purpose
optimiser that requires little pre-existing domain
knowledge. We have developed this approach over a
several years period and present results and examples
that highlight where this approach can greatly improve
the development and optimisation of complex physical
systems.",

notes = "Kodak, multiple GP runs, lead to new features in
Discipulus, land mines, ES-CDSA",